On the Convergence of a Family of Robust Losses for Stochastic Gradient Descent
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Large models with high-dimensional parameter vectors are particularly susceptible to privacy and security attacks.
“Large models, with high dimensional parameter vectors, are particularly susceptible to privacy and security attacks [158].”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [158]
Cites
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Privacy and Robustness in Federated Learning: Attacks and Defenses
Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yang, Q., & Yu, P. S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3216981 · Cited by 548 · see citing works
Mitigating Sybils in Federated Learning Poisoning
Fung, C., Yoon, C. J. M., & Beschastnikh, I. (2018) · arXiv · doi:10.48550/arxiv.1808.04866 · Cited by 385 · see citing works
PoisonGAN: Generative Poisoning Attacks Against Federated Learning in Edge Computing Systems
Zhang, J., Chen, B., Cheng, X., Binh, H. T. T., & Yu, S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3023126 · Cited by 291 · see citing works
- Authors
- Han, B., Tsang, I. W., & Chen, L.
- Affiliations
- University of Technology Sydney (AU)
- Corresponding author
- Tsang, Ivor W.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 665-680
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2346078635
- Cited by
- 25
- References
- 17
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 3 · 2021: 4 · 2020: 6 · 2019: 1 · 2018: 6 · 2017: 1 · 2016: 2
- FWCI
- 1.80
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- stochastic gradient descent, label noise, convergence rate, convex loss functions, crowdsourcing, robustness analysis
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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